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Apoptotic extracellular vesicles restore homeostasis of the articular microenvironment for the treatment of rheumatoid arthritis

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Bioactive Materials 2024年第5期35卷 564-576页

作者： Xian Li Shichun Li Xiaoling Fu Yingjun Wang School of Materials Science and Engineering South China University of TechnologyGuangzhou510641China School of Biomedical Sciences and Engineering South China University of TechnologyGuangzhou International CampusGuangzhou511442China National Engineering Research Center for Tissue Restoration and Reconstruction and Innovation Center for Tissue Restoration and Reconstruction Guangzhou510006China Key Laboratory of Biomedical Engineering of Guangdong Province South China University of TechnologyGuangzhou510006China NMPA Key Laboratory for Research and Evaluation of Innovative Biomaterials for Medical Devices Guangzhou510700China

Rheumatoid arthritis(RA)is a severe autoimmune disease with symptoms including synovial inflammation,cartilage erosion,and bone loss in RA lesions,which eventually lead to joint deformity and function *** current treatments fail to achieve satisfying therapeutic outcomes with some adverse *** vesicles derived from apoptotic cells(apoEVs)have emerged as important mediators in intercellular communication regulating diverse physiological and pathological *** this study,we investigated the therapeutic efficacy of macrophage-derived and osteoclast-derived apoEVs(Mφ-apoEVs and OC-apoEVs)on *** in vitro results showed that both Mφ-apoEVs and OC-apoEVs induced macrophage repolarization toward the anti-inflammatory M2 phenotype,promoted chondrocyte functions and chondrogenesis,and inhibited osteoclast formation and *** addition,OC-apoEVs promoted osteogenic *** in vivo study on the CIA mouse model further demonstrated that apoEVs could couple various functions and exert synergistic effects on the joint with RA,as evidenced by the regression of synovial inflammation,the reversal of cartilage damage and bone erosion,and the preservation of joint *** findings demonstrated that Mφ-apoEVs and OC-apoEVs contributed to restoring the homeostasis of the overall microenvironment in the RA joint and highlighted their potential application as a promising alternative to treat RA.

关键词： Apoptotic extracellular vesicles Microenvironment homeostasis Rheumatoid arthritis

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3D Brain Tumor Segmentation in Multimodal MRI Images

3D Brain Tumor Segmentation in Multimodal MRI Images

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IEEE international Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Nisar Ahmad Yao-Tien Chen International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

In brain tumor research, the use of machine learning based methods using MRI data to locate brain tumors accurately has significantly increased and new techniques are proposed to facilitate the medical experts. There is still room for improvement as tumors have complex anatomy which requires efforts to explore further and propose new deep learning based solutions. In this research, we propose 3D UNet based multimodal brain tumor segmentation for Non Enhancing Tumor (NET), Peritumoral Edema (PE), and Enhancing Tumor (ET). Data preprocessing and augmentation techniques are employed to optimise the model's performance. The dataset used in this study to train, validate, and test the model performance is the BraTS 2020 dataset. The model achieved improved accuracy and IoU after implementing the mentioned techniques. Results before and after data augmentation demonstrate a significant enhancement in model performance. The model achieved an improved mIoU of 0.77 and accuracies of 98.21% and 99.36% for actual data and augmented data for three brain tumor classes, i.e., NET, PE, and ET. Our model performed multiclass brain tumor segmentation with high accuracies, and we aim to enhance this study to propose a valuable medical assistance model.

关键词： Solid modeling Image segmentation Three-dimensional displays Accuracy Magnetic resonance imaging Brain modeling Data augmentation

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UNet-based Automatic Multicalss Brain Tissue Segmentation

UNet-based Automatic Multicalss Brain Tissue Segmentation

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IEEE international Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Nisar Ahmad Yao-Tien Chen International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City Taiwan

In brain research, brain tissue segmentation techniques have offered vast aid and possibilities for quantitative analysis of the brain to detect brain tumor and other brain issues. In this paper, UNet-based multiclass brain tissue segmentation is presented. To achieve the best performance of the model, data augmentation techniques are applied. The training, validation and testing of the model is performed on the BrainWeb dataset. The model has achieved improved accuracy and IoU after data augmentation. Both pre and post data augmentation results are presented which show clear improvement in model performance after data augmentation. The achieved IoU for white matter, gray matter and cerebrospinal fluid are 0.84, 0.95 and 0.92, respectively. Our model accurately performed the multiclass brain tissue segmentation and this study proposes a valuable medical aid model.

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Enhanced Deep Learning Model Performance in 3D Multimodal Brain Tumor Segmentation with Gabor Filter

Enhanced Deep Learning Model Performance in 3D Multimodal Br...

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international Conference on Applied System Innovation (ICASI)

作者： Nisar Ahmad Yao-Tien Chen International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City Taiwan

ISBN: (数字)9798350394924

ISBN: (纸本)9798350394931

medical image segmentation plays a crucial role in identifying and analyzing anatomical structures in medical images. This requires an accurate medical segmentation tool to delineate and quantitatively analyze the target regions, diagnose any abnormality, and assist in treatment planning. Deep learning approaches have emerged as a promising solution for automating medical segmentation. However, challenges arise when dealing with the complex shapes and spatial variations of some target regions, especially in 3D MRI scans. To deal with such transformations, specific techniques are required to properly analyze and preprocess the dataset and perform image filtering to provide better features for improved prediction performance of deep learning architectures. This study focuses on improving brain tumor segmentation in multimodal 3D MRI images. We observed significant improvements in multimodal brain tumor segmentation results (accuracy, IoU, and mIoU) using an optimized 3D Gabor filter, which helps extract meaningful features. Multichannel input images were preprocessed to remove noise and select an appropriate resolution to reduce computational complexity. An improvement in mean Intersection over Union (mIoU) from 0.714 to 0.804 and accuracy from 0.982 to 0.991 were achieved, which shows a major improvement. This work contributes to the field of medical image segmentation by offering an improved and efficient approach for brain tumor analysis in 3D MRI scans, potentially aiding in diagnosis and treatment planning.

关键词： Deep learning Image segmentation Solid modeling Three-dimensional displays Magnetic resonance imaging Feature extraction Planning

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Sustainable flower-like Cu-Ni metal-organic frameworks catalyst for enhanced electrochemical sensing and photocatalytic degradation of antibiotic pollutants

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Microchemical Journal 2025年 214卷

作者： N.S.K. Gowthaman Ameer Farithkhan E. Narayanamoorthi Ponnusamy Arul Chinnathambi Nandhini Vellaichamy Balakumar S. Selvarajan School of Engineering Monash University Bandar Sunway Selangor 47500 Malaysia Center for Advanced Energy Materials SRM TRP Engineering College Tiruchirapalli 621 105 Tamil Nadu India Center for Research Easwari Engineering College Chennai 600 089 Tamil Nadu India School of Materials Science and Engineering Pusan National University Busan 46241 Republic of Korea International PhD Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City 243303 Taiwan Institute of Biochemical and Biomedical Engineering Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei 106 Taiwan Department of Materials Engineering Ming Chi University of Technology New Taipei City 24303 Taiwan Department of Chemistry Sri Ramakrishna College of Arts & Science Coimbatore 641 006 Tamil Nadu India Department of Chemistry Theni Kammavar Sangam College of Arts & Science Theni 625 531 Tamil Nadu India

Massive usage of antibiotics harms the environment through animals’ excrement and squeezes open-wellbeing issues. Enrofloxacin (EF), an antimicrobial agent, treats bacterial diseases and infections in cattle, poultry and exotic animals and exhibits harmful side effects. The present work proposed a sensitive and rapid electrochemical scaffold based on the Cu-Ni bimetallic-organic framework (Cu-Ni-MOF) for the detection of harmful EF in real-time applications. The Cu-Ni-MOF was synthesized through solvothermal approach with the support of a multifunctional organic ligand, 4,4-biphenyldicarboxylicacid and characterized through various analytical tools. The electrochemical sensor for EF detection was constructed by drop-casting the Cu-Ni-MOF on a glassy carbon electrode and the fabricated sensor exhibited higher catalytic activity when compared to the parental counterparts by exhibiting 1.7-fold higher current for EF oxidation. Further, the fabricated sensor is highly active in the linear range response of 200 nM–2 mM with a limit of detection of 15.17 nM and sensitivity of 638.71 µA mM cm −2 under optimized environments. The higher electrocatalytic activity of the Cu-Ni-MOF electrode synergistically stems from the strong interaction between Cu 2+ of the MOF and pyridone and carboxylate moieties of the EF and facile electron transition facilitated by Ni 2+ . The practicability of the sensor was evaluated in milk, egg and river water samples and the obtained results were validated with Liquid Chromatography-Mass Spectrometric technique. Furthermore, the prepared catalyst was utilized for the photochemical decontamination of EF and the present Cu-Ni-MOF catalyst delivered excellent photocatalytic activity in the decontamination of EF under visible light at ambient temperature.

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High-Performance Nitrogen Selectivity through Selective Nitrate Reduction on Nickel-Supported Copper Foam Architectures

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Process Safety and Environmental Protection 2025年

作者： Thi Nhat-Tuyen Nguyen Kiruthika Mariappan Jih-Hsing Chang Tsin-Hai Wang Subramanian Sakthinathan Chao-Lin Liu Ching-Lung Chen International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City 243303 Taiwan Department of Safety Health and Environmental Engineering Ming Chi University of Technology New Taipei City 243303 Taiwan Department of Environmental Engineering and Management Chaoyang University of Technology Taichung 413310 Taiwan Department of Chemical Engineering and Materials Science Yuan Ze University Zhongli 32003 Taiwan Department of Materials and Mineral Resources Engineering National Taipei University of Technology No.1 Section 3 Chung-Hsiao East Road Taipei 106 Taiwan Department of Chemical Engineering Ming Chi University of Technology New Taipei City 243303 Taiwan R&D Center of Biochemical Engineering Technology Ming Chi University of Technology New Taipei City 243303 Taiwan Center for Environmental Sustainability and Human Health Ming Chi University of Technology New Taipei City 243303 Taiwan Center for Sustainability and Energy Technologies Chang Gung University Taoyuan 33302 Taiwan

In this study, Nickel@Copper (Ni@Cu) bimetallic electrodes were fabricated via electrodeposition to assess their efficacy in electrochemical nitrate reduction (ENR). The Ni@Cu 3m electrodes achieved a remarkable nitrate (NO 3 − ) reduction efficiency of 96.09% and nitrogen selectivity of 96.08% at a current density of 1.36 mA/cm² over an 8-hour reaction period. Extensive characterization techniques, including TEM, XPS, and XRD, were employed to analyze the structural and morphological features of the electrodes. Cyclic voltammetry was used to examine electron transfer interactions between surface nitrogen (N 2 ) species and copper foam electrodes, nitrate (NO 3 − ) reduction commenced within a potential range of −0.8 V to 0.1 V. The Ni loading on copper foam was optimized to enhance catalytic performance, and the effect of different pH levels on activity was systematically studied. The Ni@Cu 3m electrodes demonstrated excellent long-term stability over 10 cycles, maintaining high efficiency and selectivity over multiple cycles. These results highlight the strong potential of bimetallic electrodes as efficient and reliable candidates for NO 3 − reduction in wastewater treatment applications.

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Developing a Respiratory Pressure Simulation Platform for a Portable Negative Pressure Filtering Chest Drainage System

Developing a Respiratory Pressure Simulation Platform for a ...

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IEEE international Conference on Nano/Molecular Medicine and engineering (NANOMED)

作者： Hao-Yen Liao Cheng-Shane Chu Yang-Shun Wu Sheng-Chieh Lin Chia-Tsai Lin Kuo-Yung Hung Department of International Ph.D Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology TAIWAN Department of Mechanical Engineering Ming Chi University of Technology TAIWAN Research Center for Intelligent Medical Devices Ming Chi University of Technology TAIWAN

This study aims to reduce the risk of aerosol infection in patients and healthcare workers by establishing a portable negative pressure filtering chest drainage system (PNPFCDS) that filters aerosols and microparticles. By controlling the negative pressure precisely, the system can prevent aerosol transmission under different air leaks. By using a respiratory pressure simulation platform (RPSP), it has been demonstrated that the negative pressure filtering chest drainage system can achieve a minimum measured pressure error of 1.6% without simulated intrapleural pressure changes when the air leak flow rises to 800 ml/min. When the target pressure is −1.5 kPa under simulated intrapleural pressure changes with air leak of 500 ml/min, the measured pressure change rate ranges from −16.7 % to +22.7%. When the target pressure is −9 kPa, the measured pressure change rates ranges from −2% to +6.7%. According to ISO 10079-1, this study provides a model of a negative pressure filtering chest drainage system that is effective and safe.

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Interfacing bioactive glass with silk fibroin: a soft matter approach to tunable mechanics and enhanced biocompatibility

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Soft Matter 2025年 2025 May 14页

作者： Methachittipan, Apipon Vejpongsa, Ayuth Manissorn, Juthatip Khwannimit, Duangruedee Wannalobon, Thanaphum Ngambenjawong, Chayanon Damrongsakkul, Siriporn Wangkanont, Kittikhun Tonsomboon, Khaow Usaku, Chonlatep Thongnuek, Peerapat International School of Engineering Faculty of Engineering Chulalongkorn University Bangkok Pathumwan 10330 Thailand Biomedical Engineering Program Faculty of Engineering Chulalongkorn University Bangkok Pathumwan 10330 Thailand Biomedical Engineering Research Center Faculty of Engineering Chulalongkorn University Bangkok Pathumwan 10330 Thailand Center of Excellence in Biomaterial Engineering in Medical and Health Faculty of Engineering Chulalongkorn University Bangkok Pathumwan 10330 Thailand School of Biomolecular Science and Engineering Vidyasirimedhi Institute of Science and Technology (VISTEC) Wangchan Valley Rayong 21210 Thailand Department of Chemical Engineering Faculty of Engineering Chulalongkorn University Bangkok Pathumwan 10330 Thailand Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok Pathumwan 10330 Thailand National Center for Genetic Engineering and Biotechnology (BIOTEC) National Science and Technology Development Agency (NSTDA) Pathum Thani Klong Luang 12120 Thailand Research Unit on Sustainable Algal Cultivation and Applications Bio-Circular-Green-economy Technology & Engineering Center BCGeTEC Department of Chemical Engineering Faculty of Engineering Chulalongkorn University Phayathai Road Patumwan 10330 Bangkok Thailand Biomedical Materials and Devices for Revolutionary and Integrative Systems Engineering (BMD-RISE) Faculty of Engineering Chulalongkorn University Pathumwan 10330 Thailand

Tissue-engineering scaffolds must balance mechanical compatibility with biological performance to support effective tissue regeneration. Bioactive glass (BG), valued for its strength and bone-bonding ability, often suffers from high stiffness, risking stress shielding. To address this limitation, we hybridized BG with silk fibroin (SF), a soft, biocompatible protein, to create (3-glycidyloxypropyl)trimethoxysilane (GPTMS)-crosslinked BG-SF scaffolds with tunable mechanics and enhanced cellular interactions. Fabricated via the sol-gel technique with varying BG-to-SF ratios, the scaffolds demonstrated increased porosity with higher SF content, positioning SF as a natural alternative to chemical porogens. Mechanical testing revealed that incorporating SF reduced BG stiffness, improved flexibility, and enhanced toughness, aligning the scaffold properties with those of native tissues. Fatigue testing confirmed greater durability in SF-enriched scaffolds, while degradation studies highlighted controllable rates conducive to tissue regeneration. Remarkably, as little as 10 wt% SF increased cell survival by 6.5-fold in biocompatibility assays. These findings underscore the synergy between BG and SF, presenting a soft matter strategy for designing scaffolds with customizable properties for tissue-engineering applications. © 2025 The Royal Society of Chemistry.

关键词： Fiber bonding

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Colorectal Polyp Detection and Classification using Deep Learning Techniques

Colorectal Polyp Detection and Classification using Deep Lea...

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IEEE international Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Yao-Tien Chen Nisar Ahmad Chi-Che Hong M. David Honesty International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City Taiwan Department of Mechanical Engineering Ming Chi University of Technology New Taipei City Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

According to recent studies, colorectal cancer is one of the leading causes of death these days. Colorectal polyps are irregular formation of tissues that lead to colorectal cancer. Conventional methods are not sufficient to properly detect colorectal cancer in the early stages. Therefore, an efficient method is inevitable to detect and classify colorectal polyps in the early stage. In recent years, deep learning-based methods have been proposed, but there is still a need for improvement to make these methods more efficient. In this study, we have made a detailed comparison of the latest methods based on their computations and performance to propose an efficient model for real-time polyp detection. The comparison is made between deep learning based on three computation strategies: Edge Computing, General Purpose, and High Computing, using a custom colorectal dataset from a hospital. Similarly, data augmentation is also applied to analyse the performance of the mentioned techniques. We expect this study to provide insight into selecting an efficient method that would play a significant role in real-time polyp detection and classification to ensure invaluable medical aid.

关键词： Deep learning Training Learning systems Hospitals Computational modeling Colorectal cancer Data augmentation

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Using electroencephalography signals to explore the influence of Zentangle on meditation and mindfulness

Using electroencephalography signals to explore the influenc...

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international Conference on Applied System Innovation (ICASI)

作者： Muhammad Usman Chun-Ling Lin International Ph.D Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City Taiwan Department of Electrical Engineering Ming Chi University of Technology New Taipei City Taiwan

Fifteen novices completed a Zentangle task while their brain activity was measured with electroencephalography (EEG) and their concentration emotional state, stress, and anxiety levels were evaluated with questionnaires. The study found increased activity in theta, alpha, beta, and gamma ranges associated with relaxation, focus, and mental activity. Participants reported improved concentration, emotional state, and reduced stress and anxiety levels after completing the task, suggesting the potential of Zentangle to enhance cognitive and emotional well-being.

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